The world of manufacturing, including process engineering, has been under continuous and accelerating pressure to improve quality and reduce costs. This trend shows signs of further accelerating rather than decelerating. From a manufacturing perspective, quality refers to producing parts that 1.) are close to or at engineering design targets, and 2.) exhibit minimal variation. The realm of design engineering has also been under continuous pressure to improve quality and reduce costs. Design engineering must create nominal design targets and establish tolerance limits where it is possible for manufacturing to produce parts that are 1.) on target and 2.) that fall within the design tolerance limits. In-other-words, engineers are tasked not only with designing articles to meet form, fit and function, but with designing them for producibility.
In any manufacturing or other process that depends on the laws of engineering and physics to produce a useful result, there are five fundamental elements (see FIG. 1): 1) the process that makes the product (A); 2) Inputs into the process (B); 3) Output from the process (C); 4) Process control variables adjusted to influence the process output (D); and, 5) uncontrolled process variables that influence the process (E) (e.g., either uncontrollable variables or variables that are left uncontrolled because of time, cost or other considerations, collectively referred to as “noise.”).
The traditional approach to producing articles, such as parts or other components, that meet design specifications is a logical one based on a search for causation. This approach is based on the principle that, control over the variables that influence a process yields control over the output of that process. In-other-words, if one can control the cause, then one can also control the effect. FIG. 2 illustrates this prior art principle, where an attempt is made to determine the relationships, linkages, or correlations between the control variables and the characteristics of the output (e.g., manufactured parts).
Unfortunately, many manufacturing processes act like a black box. It can be difficult in some of these cases to determine the relationship between the process control variables and the resulting article characteristic values. Furthermore, time and economic constraints can make such a determination impractical even when this might be technically possible.
Plastic injection molding is an example of this situation. With at least 22 control variables, even when these control settings have only two levels each (a high and a low temperature, a high and a pressure, etc.), there are nevertheless over 4 million possible combinations. Indeed, there are billions of possible combinations when three levels (high, medium and low settings) are considered. Furthermore, changes to process variables may have varying effects on the resulting article characteristics; for example, increasing a pressure setting can increase a first article characteristic, decrease a second, and not affect a third. Simple interactions, complex interactions and non-linearities complicate the situation further. Further, there are usually multiple mold cavities in a single mold. Finally, there are numerous article characteristics (dimensional, performance, or other requirements) that must be met. In light of the preceding, it is often extremely difficult to establish the combination of factors from the large number of part design targets, part tolerance limits, mold design characteristics and injection molding press settings that produces acceptable articles.
Some progress has been made in this regard. Design of Experiments (DOE) methodology greatly reduces the number of experiments that must be conducted to understand the impact of a selected subset of control variables on the resulting output of a process. Unfortunately, even after performing a designed experiment, there are still a large number of control variables that can affect the resulting articles. In any event, extensive measurement of produced parts is still conducted by both the supplier and the OEM customer to ensure that acceptable articles are produced.
In addition, there are two main paths to achieving improved manufacturing quality. The first is to measure the parts after they are produced and then compare the parts to specification requirements (design targets and tolerances). This is an “on-line” process utilizing feedback. The parts are usually measured, to some extent, by both the producer and the customer (OEM, first tier manufacturer, second tier manufacturer, etc.). Measuring the parts, recording and analyzing the data, and reporting the results, however, is a very expensive and resource consuming process.
In their efforts to improve quality, many manufacturers have begun to use the techniques of Statistical Process Control (SPC) and Process Capability studies. Indeed, many customers require their suppliers to perform SPC or other equivalent measurement, recording, analysis and reporting procedures. According to this technique, samples are taken from the production line, measured and then analyzed to see if any abnormal (not normally distributed) patterns or data points emerge. If such abnormal data points are detected, the process is considered “out-of-control” (i.e., failing to yield a consistent predictable output) and production is immediately stopped to fix the process. The measurement data from manufactured parts is analyzed using sophisticated SPC statistical methods and charting tools embodied in specialized computer programs. Since most parts have many different dimensions, measurement and SPC analysis have usually been applied to a large number of part dimensions for each part, increasing the time and expense associated with production. However, SPC is far less expensive in the long run than shipping unacceptable parts and/or having to sort acceptable parts from unacceptable parts.
It has also been difficult for manufacturers (and their customers) to determine 1.) what percentage of the dimensions should be monitored using SPC and 2.) which dimensions should be measured if the full set of dimensions is not monitored. Usually, most, if not all, of the “critical” dimensions called out by the design engineer are measured and analyzed using SPC techniques. However, economic constraints can result in fewer than the desired number of dimensions being measured and analyzed. Guesswork is then frequently involved as to which dimensions to select for SPC or other analysis.
A second path to improving manufacturing quality is by reducing the natural variation in the manufactured articles. The accuracy of maintaining the process control factors can be improved and/or the “noise” factors can be eliminated or minimized. This is an “off-line” process improvement using feed-forward. Reducing natural variation is also an expensive proposition since many relatively small common causes of variation are present. The degree to which the natural variation in the parts produced must be reduced is usually determined through expensive process capability studies, usually conducted on each “critical” dimension.
There are numerous systems for describing and classifying manufacturing and other processes. One such system categorizes manufacturing and other processes into two broad categories—continuous and cyclical. In a continuous process, the process typically receives continuous inputs, performs the manufacturing process and generates continuous outputs. A plastic, rubber or metal extruding machine or an electrical generator would be an example of a continuous process.
In a cyclical process, the process typically receives inputs (usually material, energy, etc.), performs an operating cycle which generates the manufactured part(s), expels the manufactured part(s) and repeats the cycle. These steps can overlap. Each step in a cyclical process typically takes place over a fixed time period once the process has been adjusted and is in a stable condition. Plastic, ceramic, metal or rubber injection molding are examples of cyclical processes. Dry powder or stamping presses are additional examples of cyclical processes.
Direct control variables in a manufacturing or other process are variables that directly control or influence various mechanical, electrical, electronic, hydraulic, optical, temperature, orientation, distance, velocity, acceleration and other parameters of the process. Direct control variables can be thought of as “knobs” on a machine, process or system that directly influence or control how the machine, process or system functions. Direct control variables can be used in an injection molding machine, for example, to adjust or control the operation of physical parameters on the machine such as injection speed, hold time, barrel temperatures, clamping pressure and screw travel distance. Environmental variables also influence the output of a manufacturing or other process. Environmental variables are typically controlled when it is necessary to do so and are left uncontrolled when it is unnecessary, difficult or non-economical to do so.
Intermediate (indirect) process variables are variables that are not directly controllable by the operator or controller during the operation of the process. They are, instead, variables that change as a result of either a change in the value of one or more direct control (or environmental) variables and/or from the operation of a cyclical process. The velocity, temperature, viscosity and pressure of the plastic at any point in the flow path of a plastic injection molding machine are examples of intermediate variables in a cyclical process. These relationships are illustrated in FIG. 29 for a cyclical process and an intermediate process variable. The process operator does not have a direct control variable “knob” that directly controls any of these intermediate process variables. Instead, sensors of various types can be used to measure the value of the intermediate process variables at any point in the flow path. Controlling something is proactive. Measuring something is reactive.
Existing technology, as a further example, has developed sensors that are used in plastic injection machine mold cavities. Cavity pressure is one such example of an intermediate process variable. Cavity temperature is another example. The machine controller (or operator) does not have a “knob” that will directly control cavity pressure or temperature. The controller must, instead, adjust one or more direct control variables that will directly influence the mechanical/electrical/hydraulic/timing operation of the molding machine and subsequently and indirectly influence the cavity pressure or temperature.
In a continuous process, each direct control variable is usually set or maintained at or close to a constant value to maintain stable operations and constant output. In a cyclical process, each direct control variable is typically programmed to have a specific start and stop time during each step of the process cycle. Each direct control variable in a cyclical process can also be programmed to have a value-versus-time profile. The value of an intermediate variable can change in two different and distinct ways depending on whether the process is continuous or cyclical. A process is in stable operation when start-up transients have damped out. For a continuous process in stable operation, the value of intermediate variables will typically exhibit slight changes over time due to slight changes associated with the process, such as drift in the values of the direct control variables or drift in the process sensors or drift in uncontrolled factors such as environmental conditions or variability in the intermediate process variable sensor. This is illustrated in FIG. 30 with tolerance bands around the desired or expected value of the intermediate process variable.
The value of many intermediate variables is typically not constant for cyclical processes. For a cyclical process in stable operation—the value of the intermediate variables typically change with time as a result of the normal operation of the manufacturing process as the process moves through the various steps in its processing cycle. The value versus time profiles of the intermediate variables can further change when the values of the direct control variables drift with time. The changes in the values of an intermediate process variable can change significantly during the normal operating cycle. An intermediate variable, such as cavity pressure, for example, will typically:                1. Start at zero pressure;        2. Increase in value as plastic is forced through the nozzle, into the runner system and into the mold cavity;        3. Reach a maximum or peak pressure, which can be thousands of pounds per square inch;        4. Decrease to an intermediate pressure level;        5. Be held at the intermediate pressure level for some time period; and then,        6. Decrease back to zero pressure as the injection molding machine completes its cycle.These relationships are illustrated in FIG. 31 for a cyclical process and an intermediate process variable such as cavity pressure.        
In this cyclical process example, cavity pressure may change in a relatively repeatable or stable time-dependent pattern or profile as the machine repeatedly moves through its operating cycle. In a perfect manufacturing process (with perfect control over the direct control variables, no uncontrollable variables, no wear on the machine, perfect sensing of the intermediate variables, etc.), the cavity pressure versus time profiles would be very consistent, if not identical, from cycle-to-cycle. However, injection molding machines are not perfect. Direct control variables drift. Machines wear. Intermediate variable sensors have changing sensitivity. Outside environmental variables influence the process. The cavity pressure versus time profile will typically vary due to changes in the values of the direct control variables during the cycle and due to the just described uncontrollable or uncontrolled variables. Consequently, the cavity pressure versus time profile will fall within a band as the machine repeatedly cycles.
Upper and lower limits or tolerances or boundaries can be set for the values of one or more intermediate process variables. The intermediate process variable limits can be time-independent in a continuous system or they can be time-dependent in a cyclical system. The intermediate process limits are generally set so that the process generates product or outcomes within the part/product specification limits as long as the intermediate process variable(s) remains inside of the limits. The operating limits for an intermediate process variable are usually determined empirically or through more formal procedures such as Design of Experiments. If the value of one or more intermediate process variables falls outside of its limits in either an open-loop system or in a closed-loop control system, then alarms can be triggered and the part or process output can be rejected as an actual defective part or a potentially defective part. A closed-loop control system can use the value of one or more of the intermediate process variable to adjust the direct control variables to bring the intermediate process variable(s) back into its limits.